Authors: Mohammed Suliman Haji; Mohammed Hazim Alkawaz; Amjad Rehman; Tanzila Saba
Addresses: Faculty of Computing, Universiti Teknologi Malaysia, Skudai, 81310 Johor, Malaysia ' Faculty of Information Sciences and Engineering, Management and Science University, Shah Alam, Selangor, Malaysia ' College of Computer and Information Systems, Al Yamamah University, Riyadh, 11512, Saudi Arabia ' College of Computer and Information Sciences, Prince Sultan University, Riyadh, 11586, Saudi Arabia
Abstract: Currently, rapid growth of digital images on the internet is observed, accordingly, the need for content-based image retrieval systems are in high demand. Content-based image retrieval (CBIR) is an image search technique that does not depend on manually assigned annotations; rather, CBIR uses discriminative features to search an image. By refining features, an efficient retrieval mechanism could be achieved. The aim of this research is to review features extraction and selection that have an impact on content-based image retrieval (CBIR) and information extraction from images using global and local features such as shape, texture and colour. In order to extract most appropriate features for content-based image retrieval (CBIR), several feature extraction and selection techniques are analysed and their efficiency is compared. Additionally, shortcomings of current content-based image retrieval techniques are addressed and possible solutions are suggested to enhance accuracy.
Keywords: content-based image retrieval; CBIR; discrete wavelet transform; low-level features and high-level features.
International Journal of Computational Vision and Robotics, 2019 Vol.9 No.1, pp.14 - 38
Received: 05 Jan 2018
Accepted: 01 May 2018
Published online: 21 Feb 2019 *